Quantization of data streams of instrumented software and handling of delayed data by adjustment of a maximum delay

ABSTRACT

Described are systems, methods, and techniques for collecting, analyzing, processing, and storing time series data and for evaluating and determining whether and how to include late or delayed data points for inclusion when publishing or storing the time series data. Maximum delay values can identify a duration for waiting for late or delayed data, such as prior to publication. In some examples, maximum delay values can be dynamically adjustable based on a statistical evaluation process. For late or delayed data points that are received after the maximum delay elapses, some data points can be included in the stored time series data, such as if they are received in the same order that they are generated.

RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”).

In some examples, machine data may be generated by software or based onphysical parameters associated with a computing system on which thesoftware is operating. For example, machine data can include log data,performance data, diagnostic data, metrics, tracing data, or any otherdata that can be analyzed to diagnose equipment performance problems,monitor user interactions, and to derive other insights.

Monitoring certain machine data in real-time or near real-time may bedesirable for some applications. For example, it may be useful tomonitor performance data or metrics, such as processor usage or memoryusage, in real-time to allow for identification of problems as theyoccur. Tracking machine data in real-time or over various time periods(e.g., hours, days, weeks, months) can also allow for identification ofpatterns and can be useful for scaling resources, for example. In somecases, it can be useful to consolidate or compile machine data generatedin real-time (e.g., every second) over longer periods (e.g., minutes,hours, days, etc.) to allow for easier visualization and interpretationor analysis of the machine data.

SUMMARY

Techniques, which may be embodied herein as systems, computing devices,methods, algorithms, software, code, computer readable media, or thelike, are described herein for collecting, analyzing, processing, andstoring time series data and for evaluating time series data, such as bya data quantizer system. The data quantizer system may optionallyquantify, qualify, or otherwise indicate the occurrence of events basedon evaluation of the time series data. The time series data may comprisemachine data, which may correspond to sensor data, processing data,resource use data, network data, program data, or othercomputer-generated or derived data that may indicate software, hardware,network, performance, or other characteristics. The time series data maybe generated in real time by one or more data sources, which can relaydata points to the data quantizer system on a repeated basis and thedata quantizer system can roll-up, bucket, or otherwise aggregatemultiple data points on a fixed time interval, which can be published,stored, or communicated.

In some examples, described in further detail herein, time series datapoints from a data source may be received at a data quantizer systemafter some amount of delay after the data points are generated. Suchdelay may be associated with a network latency, for example, but delaysare not so exclusively limited. In some cases, the delays may beconsiderable and the techniques described herein provide for determiningwhether and how to include late or delayed data points when publishingor storing the time series data. In some examples, maximum delay valuescan identify a duration for waiting for late or delayed data, such asprior to publication. In some examples, maximum delay values can bedynamically adjustable based on a statistical evaluation process. Forlate or delayed data points that are received after the maximum delayelapses, some data points can be included in the stored time seriesdata, such as if they are received in the same order that they aregenerated.

In one aspect, techniques are described herein for dynamically adjustinga max delay in publishing data points for a time series of data points,including computer-implemented methods, systems or computing devices,and non-transitory computer readable media. In some examples, a methodof this aspect may comprise receiving a first data point of a timeseries of data points. The first data point can include a first datapoint raw time indicating a time of creation of the first data point anda first receipt time indicating a time for which the first data point isobtained at the data quantizer system. A first receipt delay time canalso be derived for the first data point. The first receipt delay timecan comprise a delay between the first receipt time and the first datapoint raw time.

Responsive to determining that the first receipt delay time is greaterthan any of a set of maximum delay values for the time series of datapoints a first time to live value can be derived. The first time to livevalue can specify a time for the first receipt delay time to be includedin the set of maximum delay values. A greater deviance between the firstreceipt delay time and a weighted moving average delay for the timeseries of data points can reduce the first time to live value. The setof maximum delay values can further be updated to include the firstreceipt delay time and the first time to live value. The first datapoint can be published to a streaming analytics engine at a time afteran end of a first applicable window.

The method can also include receiving a second data point. The seconddata point can include a second data point raw time indicating a time ofcreation of the second data point and a second receipt time indicating atime for which the second data point is obtained at the data quantizersystem. A second receipt delay time can be derived for the second datapoint. The second receipt delay time can include a delay between thesecond data point raw time and the second data point raw time.

Responsive to determining that the first time to live value associatedwith the first receipt delay time has expired, a second time to livevalue based on the second receipt delay time can be derived. Further,the set of maximum delay values can be updated to add the second receiptdelay time and the second time to live value to the set of maximum delayvalues and remove the first receipt delay time included in the set ofmaximum delay values. The second data point can be published to thestreaming analytics engine at a time after an end of a second applicablewindow.

In another aspect, techniques are described herein for using late datapoints when computing roll-up data points, such as data points receivedafter the expiration of a max delay value, includingcomputer-implemented methods, systems or computing devices, andnon-transitory computer readable media. In some examples, a method ofthis aspect may comprise receiving a first data point of a time seriesof data points, the first data point having a first data point raw timeand received at a first data point receipt time; identifying a firstroll-up window for the first data point based on the first data pointraw time; determining that the first data point is received after thefirst roll-up window is closed; determining that the first data point isreceived in an in-order condition based on at least the first data pointreceipt time; generating a first roll-up data point for the firstroll-up window using the first data point; and storing the first roll-updata point to one or more non-transitory data storage devices.

Roll-up data points may be published, such as by storing to a datastorage device or by transmitting to a streaming analytics system, forexample. Optionally, methods of this aspect may further comprise storingthe first data point to the one or more non-transitory data storagedevices. Optionally, methods of this aspect may further comprisepublishing the first roll-up data point generated using the first datapoint. Depending on the particular configuration, however, publicationmay occur before a late data point is received, and so some methods ofthis aspect may optionally comprise, prior to receiving the first datapoint, generating the first roll-up data point for the first roll-upwindow without using the first data point; and publishing the firstroll-up data point generated without using the first data point.

A roll-up window being in a closed condition may indicate that a timeperiod for receiving additional data points in the roll-up window hascompleted. Such a condition may occur after a max delay time followingthe roll-up window has elapsed, which may indicate a close time for theroll-up window. In some examples, determining that the first data pointis received after the first roll-up window is closed comprisesdetermining that the first data point receipt time is later than a closetime for the first roll-up window.

Data points may be received in-order or out-of-order. In-order data maycorrespond to data points that are received in the same order in whichthe data points are generated, for example. Out-of-order data points maycorrespond to data points that are received after an earlier generateddata point is received. In some cases, data points being in-order orout-of-order may not be critical, such as for data points that arereceived while the roll-up window is open. However, when a roll-upwindow is closed for receiving additional data points, late receiveddata points may, in some cases, be added to a closed roll-up window ifthey are received in order. In some examples, determining that the firstdata point is received in an in-order condition comprises determiningthat the first raw time is later than all other raw times for all otherreceived data points of the time series of data points; or determiningthat no other raw time for any other received data point of the timeseries of data points are later than the first raw time.

In other cases, late received data points may be out-of order. In someexamples, a method of this aspect may further comprise receiving asecond data point of the time series of data points, the second datapoint having a second data point raw time and received at a second datapoint receipt time; identifying a second roll-up window for the seconddata point based on the second data point raw time; determining thatsecond data point is received after the second roll-up window is closed;determining that the second data point is received in an out-of-ordercondition based on the second data point raw time and the second datapoint receipt time; generating a second roll-up data point for thesecond roll-up window without using the second data point; and storingthe second roll-up data point to the one or more non-transitory datastorage devices. In some examples, determining that the second datapoint is received in an out-of-order condition may comprise determiningthat the second raw time is earlier than at least one other raw time forany other received data points of the time series of data points.

As noted above, data points received while an assigned roll-up window isopen may not implicate needing to evaluate whether the data point islate. In some examples, a method of this aspect may further comprisereceiving a second data point of the time series of data points, thesecond data point having a second data point raw time and received at asecond data point receipt time; identifying a second roll-up window forthe second data point based on the second data point raw time;determining that second data point is received while the second roll-upwindow is open; generating a second roll-up data point for the secondroll-up window using the second data point; and storing the secondroll-up data point to the one or more non-transitory data storagedevices. A roll-up window being open may indicate that a max delay valueassociated with the window may not have elapsed or that the time isbefore the close time for the window. In some examples, determining thatthe second data point is received while the second roll-up window isopen comprises determining that the second data point receipt time isearlier than a close time for the second roll-up window.

It will be appreciated that the above described aspects may beimplemented as methods, systems, computing devices, and/ornon-transitory computer readable media. For example, a system orcomputing device may comprise one or more processors and anon-transitory computer-readable storage medium having stored thereoninstructions that, when executed by the one or more processors, maycause the one or more processors to perform operations, such asoperations corresponding to methods described herein. In anotherexample, a non-transitory computer-readable storage medium may compriseor have stored thereon instructions that, when executed by the one ormore processors, may cause the one or more processors to performoperations, such as operations corresponding to methods describedherein.

The term embodiment and like terms are intended to refer broadly to allof the subject matter of this disclosure and the claims below.Statements containing these terms should be understood not to limit thesubject matter described herein or to limit the meaning or scope of theclaims below. This summary is a high-level overview of various aspectsof the disclosure and introduces some of the concepts that are furtherdescribed in the Detailed Description section below. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used in isolation to determine thescope of the claimed subject matter. The subject matter should beunderstood by reference to appropriate portions of the entirespecification of this disclosure, any or all drawings and each claim.Other objects and advantages will be apparent from the below detaileddescription including non-limiting examples.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative examples are described in detail below with reference tothe following figures:

FIG. 1 provides a block diagram of an embodiment of an environment forcollecting, analyzing, processing, and storing time series data.

FIG. 2 provides an example table of time series data points with a maxdelay duration updated based on a fixed time duration.

FIG. 2 provides an example table of time series data points with a maxdelay duration updated dynamically.

FIG. 4 provides an overview of an example process for publishing timeseries data points based on a max delay.

FIG. 5 provides an overview of an example process for dynamicallyadjusting a maximum delay for time series data points.

FIG. 6A provides an overview of an example process derive a weightedmoving average delay and a delay variance for time series data points.

FIG. 6B provides an overview of an example process for deriving a timeto live value for a data point to be included in a set of maximum delayvalues.

FIG. 7 provides a block diagram of an example max delay adjustmentsystem.

FIG. 8 provides a block diagram of an example data quantizer system forevaluating late received data points.

FIG. 9 provides an example table of time series data points showingevaluation of late received data points.

FIG. 10 provides an overview of an example process for evaluating latereceived data points.

DETAILED DESCRIPTION

Embodiments described herein are useful for analyzing, visualizing,organizing, or otherwise using machine data, such as for purposes ofdetermining the state or condition of a system. For example, machinedata can be raw machine data that is generated by various components inIT environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine data can include logdata, network packet data, sensor data, application program data, errorlog data, stack trace data, performance data, metrics, tracing data,diagnostic data, and many other types of data.

The machine data may be organized as time series data, where each datapoint may have or be associated with one or more times or timestamps,such as a raw time, a receipt time, a publication time, etc., one ormore values, such as a numerical measurement that can change over time(e.g., processor usage, network latency, total transactions ortransactions per unit time, etc.), and optionally metadata, such as oneor more identifiers, dimensions, tags, labels, or other customproperties that may indicate characteristics of or associated with thedata that may or may not change over time (e.g., a metric name or metrictype of the data point, a server IP address or hostname, etc.). In someimplementations, a set of time series data associated with the samemetric name and dimensions may be referred to as a metric time series orMTS. Metric time series and other time series data may be used forvarious applications, such as for identification of patterns oranomalies.

1.0. Data Stream Quantization

FIG. 1 shows an overview of an example environment 100 for collecting,analyzing, processing, and storing time series data. Time series datamay be generated in real time by various data sources 110, which may bedistributed across various networks. These data sources 110 may transmitthe time series data to a data quantizer system 120, such as over anetwork, which may include a private network, a wide area network, or apublic network (e.g., the Internet). In some cases, the data quantizersystem 120 may be at a location remote from the data sources 110, thoughin some cases the data quantizer system 120 and one or more data sources110 may be at a common location, and optionally on the same localnetwork. The time series data may include data points that are generatedon a repeated basis, which may be a periodic basis (e.g., every second,every minute, etc.) or on a non-periodic basis (e.g., when a generationthreshold is reached, upon system reboot, etc.). As illustrated, thedata quantizer system 120 may include various subsystems or components,such as an intake system 125, a metadata system 130, a roll-up system135, an analytics system 140, a publishing system 150, a time seriesstorage 165, a metadata storage 170, a max delay adjustment system 175,and a late data handling system 180.

The data sources 110 may be any suitable computing devices, sensors,software systems, etc., that can generate or collect machine data orother data and transmit the machine data or other data in the form oftime series data to the data quantizer system 120, such as over one ormore network connections. The data sources 110 can include hosted orcontainerized software or services operating on cloud infrastructure,where computing resources are shared between multiple hosted software orservices. The data sources 110 can be present in a single location ordata center or distributed among various data centers, which may belocated remotely from one another.

The time series data generated by the data sources 110 may include oneor more data points. Each data point can be associated with a raw timethat can correspond to a timestamp indicating when a data point isgenerated by a data source 110, a time at which a data point istransmitted by a data source 110, and/or some other time assigned to thedata point by the data source 110.

The data quantizer system 120 may ingest or intake the time series datausing the intake system 125. The intake system 125 can receive the timeseries data from the data sources 110 and assign a receipt time to thedata points in the time series data based on a time at which the datapoints are received, which is generally different from the raw timeassociated with the data points, since there is usually some latencyassociated with transmitting the data point to the data quantizer system120 over a network. In some cases, however, a raw time and a receipttime may be the same, such as if network latency is low and/or if theprecision of the raw time and/or receipt time is larger than the networklatency (e.g., when the raw time and the receipt time have a precisionof 1 second and network latency is less than 100 ms). The intake system125 may remove extraneous information from the time series data, asdesired, and may adjust or format the time series data to a standardformat used by the data quantizer system 120, if not already soformatted.

The metadata system 130 may optionally create or assign metadata (e.g.,identifiers, dimensions, tags, labels, or the like), to the data points,such as if such metadata is not already assigned or present in the datapoints or if the metadata system 130 is otherwise assigned to or hasrules indicating such metadata is to be assigned. The metadata system130 may retrieve from or store metadata information to metadata storage170. Optionally, metadata storage 170 may store an index or rules forassociating various metadata with various time series data or componentsthereof.

The roll-up system 135 may process received data points to as totransform the data values in the received data points to roll-up datapoints including quantized values associated with one or more regulartime intervals. The regular time intervals may be as small as timeintervals associated with the received data points but may also belarger, such that multiple values for multiple data points can becombined to generate a quantized value. For example, the received datapoints may be associated with a time interval of 0.1 seconds, such that10 data points are received by data quantizer system 120 each second;the roll-up data points may be generated for every 1 second, every 5seconds, every 15 seconds, every minute, etc., on an ongoing basis.Assuming all data points are received and included in the combinationfor generating roll-up data points, each 1 second roll-up data point mayhave a quantized value generated from values for 10 data points.Similarly, each 5 second roll-up data point may have a quantized valuegenerated from values for 50 data points, each 15 second roll-up datapoint may have a quantized value generated from values for 150 datapoints, and each minute roll-up data point may have a quantized valuegenerated from values for 600 data points.

When combining values from multiple data points to generate a quantizedvalue for a roll-up data point, any desirable technique may be used. Insome cases, the quantized value may correspond to a sum of the datavalues. In some cases, the quantized value may correspond to astatistical measure of the data values, such as an average or standarddeviation. In some cases, a formula or algorithm may be used forcomputing quantized values from a plurality of data values. Roll-upsystem 135 may store roll-up data, such as in the form of time seriesdata, to time series storage 165.

To determine which data points may be combined for generating roll-updata points, each roll-up data point may be associated with a roll-upwindow. A roll-up window may correspond to a time period with a lengthof the time interval for the roll-up. Data points having a raw timefalling in the roll-up window may be assigned to the roll-up window anddata points having a raw time outside of the roll-up window may beassigned to another roll-up window. In one example, a 1 minute roll-updata point may have a 1 minute roll-up window that starts at 12:00:00and ends at 12:01:00, such that any data points having a raw timebetween 12:00:00 and 12:01:00 may be assigned to the 12:00:00-12:01:00window. In some cases, the lower end point may be inclusive while theupper end point may be exclusive (e.g., a data point with a raw time of12:00:00 will be assigned to the 12:00:00-12:01:00 window and a datapoint with a raw time of 12:01:00 will be assigned to the12:01:00-12:02:00 window and not the 12:00:00-12:01:00 window). In somecases, the lower end point may be exclusive while the upper end pointmay be inclusive (e.g., a data point with a raw time of 12:01:00 will beassigned to the 12:00:00-12:01:00 window and a data point with a rawtime of 12:02:00 will be assigned to the 12:01:00-12:02:00 window andnot the 12:02:00-12:03:00 window). Other end point assignment variationsare possible.

In some cases, it may be desirable to combine or associate differenttime series with one another, such as for various analytics purposes.Analytics system 140 may be used to generate, combine, or otherwiseaggregate data from one or multiple different time series to generate anaggregated time series that may be grouped over a common attribute(e.g., a metadata attribute). Optionally, the time series used by theanalytics system 140 to generate an aggregated time series may includeroll-up data points as generated using roll-up system 135, as describedabove. In some examples, one time series may include data points withvalues for processor usage for a particular processor core and a secondtime series may include data points with values for processor usage foranother processor core, and it is desired to determine an overall totalor average processor core usage. As another example, multiple timeseries (e.g., including roll-up data at a fixed time interval or roll-upwindow) may include data points for processor usage for variousprocessors in a particular data center and it may be desired to have atime series including an overall total or average processor usage forthe data center. The analytics system 140 can identify the time seriesthat include metadata identifying the particular data center anddetermine a sum or average of the processor usage for all the identifiedtime series on a per time interval or roll-up window basis to generateaggregated data points for an aggregated time series representing theoverall total or average processor usage for the data center. Theanalytics system 140 may optionally store the aggregated time series totime series storage 165. In some examples, the analytics system 140 mayoptionally receive input identifying aggregated time series to generateand then generate such aggregated time series in response.

As time series data from data sources 110 is received by data quantizersystem 120 on a continuing basis, the intake system 125, metadata system130, roll-up system 135, and/or analytics system 140 may perform theabove-described aspects repeatedly and optionally in real-time asadditional data points are received. As the roll-up data, optionallyincluding aggregated data, is generated, it may be desirable to publishthe data, such as by transmitting the roll-up data to a remote system orby storing the roll-up data to long term storage. For example, apublishing system 150 may publish time series data (e.g. roll-up data)by transmitting to streaming analytics system 190 or storing the data todata storage 160. The publishing system 150 may transmit or store thedata in real-time or near-real-time, such as according to a publicationschedule. In some examples, the publication schedule may indicate aroll-up data point or an aggregated data point is to be published atsome time after a roll-up window associated with the data point ends.Optionally, the publication time may be a fixed time duration after aroll-up window ends, such as 5 seconds. In some examples, however, thepublication time may be variable and determined on an ongoing basisdepending on various conditions, as described in further detail below.Optionally, publishing system 150 can retrieve stored time series datafrom data storage 160 and transmit to streaming analytics system 190, orotherwise cause stored time series data stored at data storage 160 to betransmitted to streaming analytics system 190.

Since data point raw times and receipt times may be different, some datapoints may be received after the roll-up window that the data point isassigned to, based on the raw time, ends. In some examples, such datapoints may be referred to herein as late data points. For example, adata point with a raw time of 12:01:59 may be assigned to a 1 minuteroll-up window of 12:01:00-12:02:00, but be received at 12:02:00 (orlater). It may be desirable to use such a data point when determiningthe roll-up data value for the 12:01:00-12:02:00 roll-up window eventhough the data point was received after the 12:01:00-12:02:00 roll-upwindow ends. The late data handling system 180 may be used inconjunction with the roll-up system 135 to add appropriate late datapoints when computing a roll-up data point for a roll-up window.

For example, in some cases, if a data point with a raw time falling in aroll-up window is received after the roll-up window ends but before theroll-up data point for the roll-up window is published, the roll-upsystem 135 can include the data point in the assigned roll-up window anduse the data point when computing the roll-up data point. In terms ofincluding such data points in a roll-up window, the roll-up window maybe described as “opening” at the start of the roll-up window and“closing” at the time at which data points should be received by inorder to be included. The closing time or, stated another way, time atwhich the roll-up window closes can optionally be the end of the roll-upwindow or can be some extra amount of time after the roll-up windowends. The extra amount of time after the end of a roll-up window forwhich a late data point may still be added to the roll-up window may bereferred to herein as a “max delay.” In some cases, the max delay may bea fixed amount of time. Optionally, the max delay may be the same as apublication delay. However, these are just examples and need not be thecase. Embodiments are contemplated and described below where the maxdelay changes over time. The max delay adjustment system 175 can be usedto adjust the length of the max delay, such as based on one or moreconditions. Further, other conditions may dictate a desirability forincluding late received data points in a closed roll-up window (e.g.,even after a max delay duration completes following the end of theroll-up window). For example, in some cases, late data points that arereceived in an in-order condition may be included when computing roll-updata points for a closed roll-up window, as described in more detailbelow.

The streaming analytics system 190 can be used to visualize and monitorconditions and/or machine data associated with or generated by thevarious data sources 110, such as in the form of time series data thatis collected and quantized into roll-up data points by the dataquantizer system 120. In some cases, the streaming analytics system 190can enable identification of problems, errors, or undesired performanceassociated with one or more of the data sources 110 in real-time or nearreal-time, allowing for troubleshooting and resolution of such issueswhile minimizing downtime. For example, the time series data maycomprise a processing load on each of the data sources 110, such as datasources that correspond to a plurality of servers distributed across aplurality of data centers. The data quantizer system 120 can correlateand roll-up the time-series data for different servers in different datacenters, for example, and publish the roll-up data points to thestreaming analytics system 190, which can monitor the processing usageon a per-data center basis, for example. In one example, the streaminganalytics system 190 can identify a change in processing usage at aparticular data center, which can indicate that insufficient resourcesmay be allocated or that a software fault or operational problem mayexist at the data center or with one or more of the servers. In somecases, the streaming analytics system 190 can generate a notification oralert, such as based on a trigger condition (e.g., a threshold value inthe time series data) being met. Optionally, the streaming analyticssystem 190 can generate reports based on the time series data. Forexample, the streaming analytics system 190 can generate reportsindicating processing usage for a particular duration (e.g., hours,days, weeks, or months), for one or more data centers or one or moreservers, optionally indicating trends, for example. In some cases, thestreaming analytics system 190 can obtain historical time series data,such as from the data storage 160, for purposes of visualization, reportgeneration, issue analysis, or the like.

The streaming analytics system 190 may include software applicationsthat permit users to interact with the data quantizer system 120, suchas for purposes of selection of time series data to be published to thestreaming analytics system 190, specification or assignment of metadata,identification of alert conditions or triggers, on-demand reportgeneration, selection of automatic report generation based on trigger orthreshold conditions, or the like. In some embodiments, the softwareapplication can be an internet browser, which may include client sidecode (e.g., Java Script) for accessing the data quantizer system 120. Insome embodiments, the software application is a proprietary applicationdeveloped for interacting with the data quantizer system 120.

2.0. Adjustment of Max Delay to Accommodate Late Data Points

As described above, the data quantizer system 120 can include a maxdelay adjustment system 175. The max delay adjustment system 175 canutilize statistical models to dynamically modify a set of maximum delayvalues for a time stream of data points. For example, if a data point isreceived at the data quantizer system comprising a delay (e.g., areceipt time for the data point that occurs after the end of anapplicable window for that data point), a set of maximum delay valuescan be updated to include the delay for the data point along with a timeto live value derived from a statistical model utilizing a weightedmoving average and variance in delays for the time series of datapoints. The time to live value can cause expiration of the delay for adata point to be removed from the set of maximum delay values. This canallow for dynamic adjustment of the maximum delay for the time series ofdata points, as anomalous delay values for data points are removed froma set of maximum delay values.

In many cases, systems can assign static delay value with a time seriesof data points. In these cases, as a delay for each data point iscalculated, a greatest delay time can be assigned as the maximum delayfor the time series of data points. However, in instances where a largedelay is identified (e.g., a 14 minute delay when a previous averagedelay time is 5 seconds), the maximum delay can include the large delayvalue. Accordingly, in such cases, the maximum delay can comprise such alarge value for a specified time duration (e.g., one hour). A staticmaximum delay value can impact a balance between timeliness (e.g.,promptly publishing data to a streaming analytics system) andcompleteness (e.g., providing a maximum amount of data points to bepublished to the streaming analytics system. The static maximum delaycan modify the balance between timeliness and completeness, as a largemaximum delay value can undesirably favor completeness over timelinessby delaying publishing of data points, for example.

The present embodiments provide for a dynamically adjustable maximumdelay value for a time series of data points. The data quantizer systemcan efficiently balance timeliness and completeness in publishing datapoints by adjusting a maximum delay values near an average delay for thetime series of data points. Responsive to an increase in a delay, thedata quantizer system can utilize a statistical model to derive a timeto live for the delay to be included in a set of maximum delays, whichcan efficiently bring a maximum value near an average delay to maximizethe data points being published to a streaming analysis system.

2.1. Example Illustrating Maximum Delay Behavior in a Data QuantizerSystem

As described above, a maximum delay can be associated with each timeseries of data points. FIG. 2 provides a first example table 200 forpublishing data points in a time series of data points. The firstexample table 200 as described with FIG. 2 provides a static maximumdelay value as is provided in some cases. The static maximum delay valuecan comprise a greatest detected delay value for the data points of atime series of data points.

As shown in FIG. 2 , the first table 200 provides a series of datapoints included in a time series of data points (e.g., data points 214,216, 218). Each data point of the time series of data points can relateto a measurement or metric. For example, the time series of data pointscan represent a CPU utilization for a machine or a number of processingtasks processed during a time duration. The time series of data pointscan be published to a streaming analytics engine for near real-timegraphical representation of one or more measurements/metrics.

As each data point is obtained, the data quantizer system can identifymetadata associated with each data point. For instance, the dataquantizer system can identify a data point raw time 202 for each datapoint. The data point raw time 202 can include a time that the datapoint is created or assigned a timestamp. For instance, the data pointraw time 202 for each data point can be assigned at a client computerprior to providing the data point to the data quantizer system. As anexample, a first data point raw time 214 a can include a time (in ahour: minute format) of 12:00, indicating a time when the data point wascreated. The data points can be assigned data point raw times 202 at anyfrequency as the data points are created by the client device.

Further, the data quantizer system can identify other applicable datarelating to each data point. For instance, an applicable window 204 canbe identified for each data point. The applicable window 204 can includea time duration in which data with a corresponding data point raw time202 falls within the applicable window. The applicable window 204 caninclude any of a variety of time durations, such as 1 minute, 5 minutes,1 hour, etc.

As an example, a first applicable window 214 b can include a timebetween 12:00 and 12:01. In this example, as a first data point raw time214 a comprises 12:00, the first data point falls within the applicablewindow 214 b. The data quantizer system can aggregate all data pointswith a raw time 202 falling within the applicable window 204 to bepublished at a window publishing time 210.

The data point receipt time 206 can include a time (in a hour: minute:second format) that the data point is received at the data quantizersystem. As a first example, a first data point can have a data point rawtime 214 a of 12:00 and a data point receipt time 214 c of 12:00:18,which comprises an 18 second delay from creation of the data point tothe receipt of the data point at the data quantizer system. In thisexample, the first data point can be included in the applicable window214 b and can be published at the window publishing time 214 e.

In another example, the data point receipt time 206 can occur after theend of the applicable window 204. For instance, a second data point caninclude a data point raw time 216 a of 12:02 and an applicable window216 b of 12:02-12:03. However, in this example, the second data pointcan include a receipt time 216 c of 12:16:18, over 13 minutes after theend of the applicable window 216 b. This delay in receipt of the datapoint can result in either a delay in publication of data points in theapplicable window 216 b or leaving out the data point from beingpublished with other data points in the applicable window 216 b.

The max delay 208 can provide a maximum delay time (e.g., a delay from areceipt time for a data point and an end of the applicable window thatthe data quantizer system will wait to publish data points in theapplicable window. In the example as illustrated in FIG. 2 , the maximumdelay is static, where a greatest detected delay for a time duration(e.g., for the past hour) is the maximum delay. For example, afterreceipt of the first data point 214, the maximum delay 214 d can include5 seconds (e.g., the difference between the window publishing time 214 eof the data point and the end of the applicable window (e.g., 12:01) 214b.

Further, in this example, the receipt of the second data point canincrease the maximum delay value. For instance, the second data point216 has a receipt time 216 c of 12:16:18, which provides a 14 minutedelay between the receipt time 216 c and the publishing time 216 e(e.g., 12:17:05) after the end of the applicable window 216 b (e.g.,12:03). Accordingly, after receipt of the second data point, the maxdelay 208 can be updated to 14 minutes.

In this example, as no other detected delay exceeds 14 minutes, the maxdelay 208 remains at 14 minutes until a delay reset 220 at 01:00.Accordingly, even if the delay from the end of the applicable window 212on average drops below the maximum delay, the max delay 208 remains thesame. For instance, a third data point 218 includes a delay form the endof the applicable window 218 f of 1 minute.

The static maximum delay value as represented in the example in FIG. 2can negatively impact the balance between completeness and timeliness.For instance, if the max delay 208 increases to 14 minutes, this mayallow for excessive delay in publishing data points in applicablewindows 204. A large static maximum delay can result in completenessbeing favored over timeliness, causing delays in publishing data pointsand presentation of the data points by the streaming analytics system.

In the present embodiments, the data quantizer system can dynamicallyadjust maximum delay value based on a statistical model for the timeseries of data points. FIG. 3 provides a second example table 300representing publication of data points with a dynamically modifiedmaximum delay value. As shown in FIG. 3 , rather than a static maxdelay, the time series of data points can include a set of adjusted maxdelay values 308. The set of adjusted max delay values 308 can includeone or more detected maximum delay values and associated time to livevalues for each of the detected maximum delay values derived from astatistical model as described herein.

For instance, a first data point 314 can include a first delay from anend of the applicable window 314 f of 5 seconds, and the set of adjustedmax delay values 314 d can include the 5 second delay. In this example,responsive to a second data point 316 having a delay 316 f of 14minutes, the delay can be added to the set of adjusted max delay values316 d (e.g., 5 seconds, 5 seconds, 14 minutes). A derived time to livevalue can be assigned to the 14 minute delay such that the 14 minutedelay value is removed from the set of adjusted max delay values afterexpiry of the time to live value. Further in this example, after expiryof the time to live value (2 minutes) the adjusted max delay values 308can remove the 14 minute delay value and replace it with a delay 312detected for a next data point. This allows for dynamic adjustment ofthe maximum delay for the time series of data points, providing greaterbalance between completeness of published data and timeliness inpublishing the data points.

In the example as illustrated in FIG. 3 , a set of maximum delay values(e.g., adjusted max delay values 308) can specify a maximum delay forthe time series of data points. For instance, a first data point 314 acan include a data point raw time of 12:00 and a data point receipt time314 c of 12:00:18. The applicable window of data points can be published(e.g., 314 e) at 12:01:05, comprising a 5 second delay from the end ofthe applicable window (e.g., 314 f). In this example, a second exampledata point can include a delay from the end of the applicable window of5 seconds, which can be added to the set of maximum delay values. Theset of maximum delay values can include a predetermined number ofvalues, such as three values, for example.

In this example, a third data point can include a data point raw time316 a of 12:02 and a data point receipt time 316 c of 12:16:18, whichcomprises a delay of 14 minutes. Accordingly, a delay from an end of theapplicable window 316 f can include 14 minutes, which can be added tothe set of adjusted delay values 316 d.

As described above, each maximum delay value in the set of maximum delayvalues can include a time to live (ttl) value, indicating a time foreach maximum delay value to remain in the set of maximum delay values.Further, as a maximum delay value deviates from a weighted average delayvalue and a delay variance value, the ttl value can be lower, allowingfor a shorter time for an anomalous delay value to remain in the set ofmaximum delay values. In the present example, the 14 minute delay valueadded in the set of maximum delay values 316 d can include a short timeto live value (e.g., 2 minutes) to remove the value after the expirationof the ttl value. For instance, at a later time point 2 minutes afterthe addition of the 14 minute delay into the set of adjusted max delayvalues 316 d, the ttl value for the 14 minute delay value can expire(e.g., 318), thereby removing the 14 minute delay value for asubsequently-derived delay value (7 seconds). This process can berepeated to remove any anomalous delay values and bring the adjusteddelay values 308 near the weighted moving average delay.

The adjusted max delay values 308 can be dynamically modified based on astatistical model to bring the maximum delay values near a weightedmoving average delay and delay variance. This can allow for windowpublishing times 210 to be published near an end of an applicable window204, balancing timeliness and completeness of data provided to astreaming analytics system providing near real-time data analysis.

2.2. Overall Process

FIG. 4 is a flowchart 400 for a process of publishing data points basedon a maximum delay for the time series of data points. As noted above, amaximum delay is a threshold time for data to be published.

At 402, the data quantizer system can receive a data point of a timeseries of data points. For instance, the data quantizer system canreceive the data point from a client device via a communication networkand extract metadata for the data point. Metadata for the data point caninclude a data point raw time (e.g., 202), a data point receipt time(e.g., 206), and an applicable window (e.g., 204), for example.

At 404, the data quantizer system can determine a receipt delay time forthe data point. The first receipt delay time can include a delay betweenthe receipt time and a window publishing time associated with the datapoint.

As an illustrative example, a data point can have a raw time of 12:00and an applicable window of 12:00-12:01. In this example, the data pointcan include a receipt time of 12:01:20. The data quantizer system canthen determine a receipt delay of 20 seconds (e.g., the receipt timeless than the end of the applicable window.

At 406, the data quantizer system can determine whether the receiptdelay is greater than a maximum delay for the time series of datapoints. In the example provided above, if the receipt delay is 20seconds and the maximum delay is 1 minute, the data point is publishedat a publishing time to a streaming analytics system (e.g., at 410).However, if the receipt delay is 20 seconds but the maximum delay is 10seconds, the data point is not published to the streaming analyticssystem (e.g., at 408). The maximum delay can balance the completeness ofdata points being published to the streaming analytics system andtimeliness in publishing the data points to the streaming analyticssystem.

At 408, if receipt delay is greater than max delay, the data point isnot published to the streaming analytics system. Such late data pointscan be dropped because of the maximum delay and maintaining timelinessin publishing data points. Rather, the data point can be stored in astorage module (e.g., data storage 160) for subsequent analysis/query.

At 410, the data point can be published at the publishing time to thestreaming analytics system. Data points for each applicable window canbe published at a publication time occurring after the end of theapplicable window. For example, if the applicable window closes at12:01:00, the data can be published at a publication time of 12:01:05.Publishing data point associated with the applicable window can allowfor the streaming analysis system to provide one or more graphicalinterfaces providing a representation of the one or more data points fornear real-time analysis.

2.3. Dynamic Max Delay Adjustment Overview

FIG. 5 provides a flow process 500 of an example method for dynamicallyadjusting a maximum delay for a time series of data points. As describedherein, the maximum delay can be adjusted using a statistical model thatincorporates a weighted moving average delay and a delay variance forthe time series of data points.

At 502, the data quantizer system can derive a weighted moving averagedelay value and a delay variance value for the time series of datapoints. The weighted moving average delay can include an average delayfor the data points during a specified time duration. For example, if aseries of data points each include a delay of 5 seconds, the weightedmoving average delay can be 5 seconds. However, in this example, if asubsequent data point has a delay of 10 minutes, the weighted movingaverage delay can increase due to the increased delay time.

The delay variance value can include a deviance between detected delayvalues in the time series of data points. In the example above, for aseries of data points each including a delay of 5 seconds, the delayvariance value can increase upon detecting a subsequent data pointhaving a delay of 10 minutes. The weighted moving average delay and thedelay variance can be utilized in deriving a time to live value for adelay added to a set of maximum delay values, as described below.Deriving the weighted moving average delay and the delay variance isdiscussed in greater detail with respect to FIG. 6A.

At 504, the data quantizer system can receive a data point. The data caninclude a data point raw time (e.g., 202 in FIG. 2 ) indicating a timeof creation of the data point and a receipt time (e.g., 206 in FIG. 2 )indicating a time for which the data point is obtained. The dataquantizer system can process metadata included in the data point toderive the data point raw time and identify the time series of datapoints for the received data point.

At 506, the data quantizer system can derive a receipt delay time forthe data point. The receipt delay time can comprise a time duration(e.g., a delay) between the receipt time (e.g., 206 in FIG. 2 ) and theend of the applicable window (e.g., applicable window 204 in FIG. 2 ).For example, the data point can include a raw time of 12:00 (and anapplicable window between 12:00 and 12:01) and a receipt time of12:01:05. In this example, the receipt delay time can comprise 5 seconds(e.g., the receipt time (12:01:05) less than the end of the applicablewindow 12:01).

At 508, the data quantizer system can determine if the receipt delaytime is greater than a set of maximum delay values for the time seriesof data points. As noted above, the set of maximum delay values caninclude one or more (e.g., three) maximum receipt delay times detectedfor data points in the series of data points. Any of the set of maximumdelay values can include a corresponding time to live value specifying atime of expiration of the value included in the set of maximum delayvalues. Determining if the receipt delay time is greater than the set ofmaximum delay values can include identifying if the receipt delay timeis greater than any value included in the set of maximum delay values.In some instances, if any of the values included in the set of maximumdelay values are removed (e.g., due to a corresponding time to live timeexpiring), the receipt delay time can be added to the set of maximumdelay values as described herein.

At 510, the data quantizer system can derive a time to live (ttl) valuefor the data point. The ttl value can be derived based on the receiptdelay value and the weighted moving average delay and the delayvariance. Particularly, a z-score can be derived based on the receiptdelay, weighted moving average delay, and the delay variance, such thata larger deviance of the receipt delay from the average delay andvariance lowers the ttl value for the data point and faster expirationof the delay from the set of maximum delay values. Deriving the ttlvalue for the data point is discussed in greater detail with respect toFIG. 6B.

At 512, the data quantizer system can modify the set of maximum delayvalues to include the receipt delay value and the derived time to livevalue. For instance, a lowest delay value in the set of maximum delayvalues can be removed for the receipt delay value and the derived timeto live value. The time to live value can provide a time of expirationfor the receipt delay value from the set of maximum delay values.Responsive to the time to live value expiring, the corresponding delayvalue can be removed from the set of maximum delay values and replacedwith a newly-derived delay value.

At 514, the data point can be published to a streaming analytics system.One or more data points in the time series of data points thatcorrespond to the applicable window can be published to the streaminganalytics system at a time after the end of the applicable window (e.g.,window publishing time 210). The streaming analytics system can presentone or more graphical interfaces providing a graphical representation ofthe time series of data points.

At 516, the weighted moving average delay value and the delay variancevalue for the time series of data points can be updated using the datapoint. The weighted moving average delay value and the delay variancevalue for each data point, providing continuously-updated metrics forthe time series of data points. Updating the moving average delay valueand the delay variance value is discussed in greater detail with respectto FIG. 6A.

2.4. Weighted Moving Average Delay and Delay Variance Derivation

As noted above, a weighted moving average delay and a delay variance canbe updated for each data point received at the data quantizer system.FIG. 6A illustrates a process 600 a to derive both the weighted movingaverage delay and a delay variance for a time series of data points.

At 602, a delta value can be derived as a delay less than an initialinstance of the weighted moving average delay. The weighted movingaverage delay can include a difference between the receipt time for thedata point (e.g., 206 in FIG. 2 ) and a raw time for the data point(e.g., 202 in FIG. 2 ). The delay can represent a time duration betweencreation of the data point and receipt of the data point at the dataquantizer system.

At 604, an updated instance of the weighted moving average delay can bederived as a summation of the initial instance of the weighted movingaverage delay and a product of the delta value (as derived in 602) andan alpha value. The weighted moving average delay can be continuallyupdated for each data point, providing a rolling average delayidentified in the time series of data points. The alpha value cancomprise a value between 0 and 1 to make the calculation of the updatedinstance of the weighted moving average delay less sensitive toanomalous delay values detected for the time series of data points.

At 606, an updated instance of the delay variance can be derived. Thedelay variance can quantify a spread of delay values for the time seriesof data point and can be indicative of an abnormality of a given datapoint. The updated instance of the delay variance can include a productof the alpha value and an initial instance of the variance value, thealpha value, and the delta value squared.

2.5. Time to Live Value Derivation

As described above, responsive to a delay for a data point exceeding anyof a set of maximum delay values, the maximum delay values can beupdated with the delay for the data point with a time to live value. Thetime to live value can specify a time that the delay is included in theset of maximum delay values such that the delay is removed from the setof maximum delay values responsive to expiration of the time to livevalue. The time to live value can be modified based on a deviance of thedelay relative to the moving average delay and delay variance such thatan anomalous delay value can be quickly removed from the set of maximumdelay values. FIG. 6B provides a process 600 b for deriving a time tolive value for a data point to be included in a set of maximum delayvalues.

At 608, a z-score can be derived. The z-score can measure how outlyingor anomalous the current delay value is relative to previous delayvalues in the time series of data points. The z-score can include avalue comprising a quotient of the delay value and the weighted movingaverage delay and a square root of the delay variance.

At 610, the time to live value can be derived. The time to live valuecan include a quotient of a maximum expiry time and a maximum of 1 andthe z-score. The maximum expiry time (maxExpiryTime) can set an upperbound for a ttl value to guarantee that the delay value is to expireafter the maximum expiry time. Similarly, a minimum expiry time(minExpiryTime) can set a lower bound for the ttl value. In someinstances, a delay value that is close to the weighted moving averagedelay may include a ttl value close to the maxExpiryTime.

At 612, if the ttl value is less than the minExpiryTime, the ttl valuecan comprise the minExpiryTime. This can prevent a ttl value being belowa threshold time and frequent. Any of the maxExpiryTime andminExpiryTime can be configurable and modified based on trends in thereceipt delays for a time series of data points.

At 614, the set of maximum values can be updated to include the delayvalue and the time to live value. For instance, a smallest delay valueincluded in the set of maximum delay values can be replaced with thedelay value and the ttl value for the data point. In some instances,responsive to a delay value being removed from the set of maximum delayvalues (e.g., due to expiration of a corresponding ttl value), the delayvalue and the ttl value for the data point can be added to the set ofmaximum delay values.

2.6. System Overview

FIG. 7 is a block diagram of an example max delay adjustment system 775,which can be the same as max delay adjustment system 175, or it can bedifferent. As noted with respect to FIG. 1 , max delay adjustment system775 can be included in a data quantizer system 120. The max delayadjustment system 775 can dynamically adjust a set of maximum delayvalues based on detected delays in a time series of data points asdescribed herein.

The max delay adjustment system 775 can include a receipt delaydetection system 702. The receipt delay detection system 702 can derivea receipt delay (e.g., 212 in FIG. 2 ) comprising a difference betweenthe receipt time of the data point and a raw time of the data point. Thedata quantizer system 120 can process the data point to identify themeta data for the data point, such as identifying the data point rawtime (and corresponding applicable window) and assigning a receipt timefor the data point. Determining the receipt delay is discussed ingreater detail with respect to 404 in FIG. 4 .

The max delay adjustment system 775 can also include a weighted movingaverage delay and delay variance monitoring system 704. The weightedmoving average delay and delay variance monitoring system 704 cancontinually update the weighted moving average delay and delay variancefor each received data point. For instance, as the delay values changefor data points, the weighted moving average delay and delay variancevalues may be modified for the time series of data points. The weightedmoving average delay and delay variance can be used in generation of attl value as described herein. Updating the weighted moving averagedelay and delay variance values for a data point is described in greaterdetail at 516 in FIG. 5 .

The max delay adjustment system 775 can also include a time to live(ttl) value generation system 706. The time to live (ttl) valuegeneration system 706 can generate a ttl value responsive to determiningthat the receipt delay exceeds any of a set of maximum delay values forthe time series of data points. Generation of the ttl value is discussedin greater detail at 510 in FIG. 5 and at FIG. 6B.

The max delay adjustment system 775 can also include a maximum valueupdating system 708. The maximum value updating system 708 can maintainand update a set of maximum delay values comprising the maximum delayfor the time series of data points. The maximum value updating system708 can also monitor ttl values for the set of maximum delay values andremove a delay value responsive to a corresponding ttl value expiring.Updating the set of maximum delay values is discussed in greater detailin 512 of FIG. 5 .

The max delay adjustment system 775 can provide the set of maximum delayvalues to each of a publication system 750 and a late data handlingsystem 780. The publication system 750 may be the same as thepublication system 150 depicted in FIG. 1 , or it may be different. Thelate data handling system 780 may be the same as the late data handlingsystem 780 depicted in FIG. 1 , or it may be different. In someexamples, the publication system 750 can utilize the set of maximumdelay values for publishing data points (e.g., by sending the datapoints to the streaming analytics service and/or storing the datapoints). The late data handling system 780 can also utilize the set ofmaximum delay values in determining an operation to perform with respectto a data point received after an end of an applicable window, forexample.

3.0. Processing of Data Points Received after Max Delay

As described above, the data quantizer system 120 can include a latedata handling system 180. The late data handling system can analyze datapoints that are received after the close of the window to which the datapoints are assigned to determine if they should be added to the assignedwindow or if they should be dropped. For example, in some cases, datapoints that arrive after the close of the assigned window or after thepublication time for the assigned window (e.g., late data points) mightnormally be dropped, but circumstances may dictate that it may beappropriate to instead add the late data to the assigned window, such asif the late data points are received in an in-order condition. This canbe beneficial for allowing a late data point to be added to an assignedwindow and dropping fewer late data points, particularly where the latedata point or data points being added to the assigned window do notimpact the validity, quality, or accuracy, of the other data points inthe assigned window or other data points in other windows.

Late arriving data can be caused by a variety of circumstances. Forexample, data may be delayed during transmission over a network, such asin the case of

FIG. 8 shows an overview of an example data quantizer system 820 forcollecting, analyzing, processing, and storing time series data, andparticularly for evaluating late data points and, if appropriate, foranalyzing, processing, and/or storing the late data points. Dataquantizer system 820 can correspond to or be the same as the dataquantizer system 120 depicted in FIG. 1 , or it can be different. Asillustrated in FIG. 8 , data quantizer system 820 includes a roll-upsystem 835, an analytics system 840, a publishing system 850, a maxdelay adjustment system 875, and a late data handling system 880.Without limitation, data quantizer system 820 may optionally furtherinclude one or more of an intake system, a metadata system, a timeseries storage, a metadata storage, or other systems, subsystems, orcomponents, but such components are not shown in FIG. 8 so as not toobscure other details. Late data handling system 880 is further shown asincluding a roll-up window identifier 881, a roll-up window evaluator882, and an order evaluator

As described above, time series data can correspond to a series of datapoints each having an associated raw time, as assigned by the particulardata source from which it originates. The data points can be received atthe quantizer system 820 and be assigned a receipt time, such as by anintake system. The receipt time can be used by the roll-up windowidentifier 881 to identify the roll-up window for any received datapoint. In some examples, the roll-up system 835 may establish variousroll-up windows, such as based on the various roll-up data points theroll-up system 835 is determining. For example, for time series datagenerated by a data source on a repeated 1 second basis and for whichroll-up time series data are to be generated on various length roll-upwindows, such as 15 second roll-ups, 1 minute roll-ups, 5 minuteroll-ups, and 10 minute roll-ups, the roll-up system 835 may establish aseries of roll-up windows for each of the roll-up time series data.These windows may be used, in association with the raw time of a datapoint, by roll-up window identifier 881 to determine which roll-up datapoint and roll-up window a late received data point should be assignedto.

The assigned roll-up window may further have a window close time, whichmay be determined based on the end time of the assigned roll-up windowand a max delay value, which can indicate an additional amount of timeafter the end of a roll-up window for which the roll-up window is stillopen for receiving added data points and after which the roll-up windowscloses. In some examples, the max delay value may be a fixed amount oftime, and may be a fraction of the length of the roll-up window, or amultiple of the length of the roll-up window, such as in the case of aroll-up window the same or close to the time spacing between datapoints, or may be a specific fixed value. In some examples, the maxdelay value may be a variable or dynamically generated value, asdescribed above, such as determined by the max delay adjustment system875, which may be similar to or the same as the max delay adjustmentsystem 175 or the max delay adjustment system 775, or it may bedifferent.

Having the raw time for a received data point, the receipt time for thedata point, the assigned roll-up window for the data point, and thewindow close time, the roll-up window evaluator 882 can determine if theroll-up window is open for adding the received data point. Such processcan include identifying if the window close time is after the instanttime or if the window close time is earlier than the instant time. Suchprocess can include identifying if the window close time is earlier thanthe receipt time for the data point or if the window close time is laterthan the receipt time for the data point. In the event that the roll-upwindow is still open for receiving new data points (e.g., if the receipttime is earlier than the window close time), the data point can besimply added to the roll-up window. This situation corresponds to thenormal circumstances for received data points that are not late (e.g.,having a receipt time that is not later than the window close time). Insuch a case the data point may be passed to the roll-up system 835 foraddition to the roll-up window or to the analytics system 840 for use incomputing aggregated data.

In the event that the roll-up window is closed for receiving new datapoints, (e.g., if the receipt time is later than the window close time),further evaluation may be needed. In conventional practice, data pointsthat are received after the close of a roll-up window may be handled invarious ways. In one example, the data point may be assigned to andadded to a following roll-up window if the receipt time is after theclose of the originally assigned window or if the receipt time is afterthe publication time for the originally assigned window. In anotherexample, the data point may be dropped. if the receipt time is after theclose of the originally assigned window or if the receipt time is afterthe publication time for the originally assigned window.

However, in examples described herein, some data points may be added totheir respective assigned windows despite having a receipt time that isafter the close of the assigned window or having a receipt time that isafter the publication time for the assigned window. For example, orderevaluator 883 may analyze a late received data point to determine if thedata point is received in an in-order condition or an out-of-ordercondition. As used herein, an in-order condition corresponds to statewhere a data point with a first raw time is received in sequence withother data points from the same time series data, arriving after otherdata points having raw times earlier than the first raw time. In somecases, an in-order data point having a first raw time may be receivedprior to other data points having raw times later than the first rawtime. An out-of-order condition, in contrast, corresponds to a statewhere a data point with a first raw time is received after data pointswith later raw times. In some examples, order evaluator 883 may compareraw times for a data point being evaluated with raw times for other datapoints added to the assigned roll-up window to determine whether the rawtime for the data point being evaluated is earlier or later than any ofthe raw times for the other data points in the roll-up window. When thedata point being evaluated has a raw time after all other data pointsadded to the roll-up window, the data point may be determined to be anin order condition and may be passed to the roll-up system 835 foraddition to the assigned roll-up window or to the analytics system 840for use in computing aggregated data. When the data point beingevaluated has a raw time earlier than any other data points added to theroll-up window, the data point may be dropped.

FIG. 9 provides an example table 900 of characteristics for a sequenceof data points in a time series data, showing the point raw times 902,assigned windows 904, window close times 906, window publishing times908, data point receipt times 910, indicators of whether a data point islate (late indicator 912), indicators of whether a data point isreceived in an in-order condition or an out-of-order condition (orderindicator 914), and indicators of whether a data point is to be added tothe assigned window or dropped (add/drop indicator 916). Point numbers918 are also indicated in the example table 900 for convenience ofdiscussion herein, but point numbers may or may not be assigned or usedby or in a data quantizer system.

In this example, a series of 15 data points are generated, once persecond, and these data points are to be rolled-up in 5-second roll-upwindows. The max delay in this example is fixed at 1 second, but asdescribed above this can vary in other examples. Here, the windowpublishing time is fixed at 1 second after the window close time. Forexample, raw times 902 are indicated for each point; for point number 1the raw time is 12:00:00, for point number 2 the raw time is 12:00:01,and so on. An assigned window 904 for each data point is determined,based on the raw time. For example, points 1-5, having raw times12:00:00-12:00:04, are assigned to the window of 12:00:00-12:00:05,points 6-10, having raw times 12:00:05-12:00:09, are assigned to thewindow of 12:00:05-12:00:10, and so on. The window close time 906 foreach point and window is indicated as 1 second after the window ends,and the window publishing time 908 for each point and window isindicated 2 seconds after the window ends and 1 second after the windowcloses. For example, the 12:00:00-12:00:05 window has a window closetime of 12:00:06 and a window publishing time of 12:00:07, the12:00:05-12:00:10 window has a window close time of 12:00:11 and awindow publishing time of 12:00:12, and the 12:00:10-12:00:15 window hasa window close time of 12:00:16 and a window publishing time of12:00:17.

Data point receipt times 910 are indicated, with point numbers 1, 2, 6,7, and 8 being received 1 second after their raw times, point numbers 4and 5 being received 2 seconds after their raw times, point number 3being received 3 second after its raw time, point number 15 beingreceived 6 seconds after its raw time, point number 12 and 14 beingreceived 7 seconds after their raw times, and point numbers 9, 10, 11,and 13 being received 8 seconds after their raw times.

Based on comparing the receipt times 910 and the window close times 906,points 1-8 are determined to be on time and not late, such that lateindicator 912 for these points is “no”; for example, the receipt times910 for points 1-8 is before the respective window close times 906.Based on comparing the receipt times 910 and the window close times 906,points 9-15 are determined to be late, such that late indicator 912 forthese points is “yes”; for example, the receipt times 910 for points9-15 are after the respective window close times 906.

Based on the receipt times 910 and the raw times 902, points 1-3, 5-10,and 12-15 are determined to be received in an in-order condition andpoints 4 and 11 are determined to be received in an out-of-ordercondition, as indicated in the order indicator 914. For example, point 1is the first point received, so it is in-order, by default, since noother points with later raw times can have earlier receipt times at thetime point 1 is received. At the time of receipt of points 2-3, 5-10,and 12-15, their raw times 902 are later than the raw times 902 for allother received points, so they are also in-order. At the time of receiptof points 4 and 12, however, their raw times 902 are earlier than theraw times for other received points. Looking at this from anotherperspective, ordering the points by their raw times gives:

1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15; ordering the pointsby their receipt times gives:

1, 2, 3, 5, 4, 6, 7, 8, 9, 10, 12, 11, 13, 14, 15. Comparing the orderof these points shows that point 4 is received after point 5, but has araw time before point 5. Similarly, point 11 is received after point 12,but has a raw time before point 12.

Based on the late indicator 912 and the order indicator 914, theadd/drop indicators can be determined. For all points that are not late(e.g., where the late indicator 912 is “no”), these points can be addedto their respective assigned windows. This will be the case for point 5,even though the order indicator for point 5 is “out-of-order.” In someexamples, when a window is open for adding new data points, the assigneddata points can be added to the window even if they are received in anout-of-order condition. For the points that are late (e.g., where thelate indicator 912 is “yes”), further evaluation of the order indicator914 can be used to determine whether to add the points to theirrespective window or to drop the points. In some examples, for pointsthat are late and are received in an in-order condition, these pointscan be added to their respective assigned windows (e.g., the add/dropindicator will be “add”). In some examples, for points that are late andare received in an out-of-order condition, these points can be dropped,meaning they are not added to their respective assigned windows.

Depending on the publishing time of the window, various situations maybe performed by publishing system 850. For example, if a late receiveddata point in an in-order condition is added to its assigned windowbefore the window is published, the roll-up data point for the windowcan be determined by roll-up system 835 using the late received datapoint and subsequently published by publishing system 850. In such asituation, the publication can result in transmission of the roll-updata point to a streaming analytics system and/or storage of the roll-updata point to a data storage device, such as data storage 860. However,when publication occurs before receipt of a late data point in anin-order condition, the roll-up data point for the window can beinitially determined by roll-up system 835 without using the latereceived data point and published by publishing system 850, such aswhere the roll-up data point (determined without using the late receiveddata point) is transmitted to a streaming analytics system and/or storedto a data storage device, such as data storage 860. Once the latereceived data point in the in-order condition is added to its assignedwindow, a new roll-up data point for the window can be determined byroll-up system 835, now using the late received data point. Publicationby publishing system 850 can result in the new roll-up data point beingstored to a data storage device, such as data storage 860, optionally inplace of the original roll-up data point determined without using thelate received data point. In some cases, publishing system may nottransmit the new roll-up data point to the streaming analytics system ormay only transmit the new roll-up data point to the streaming analyticssystem on request.

Turning next to FIG. 10 , a flow chart providing an overview of anexample method 1000 is shown. Method 1000 begins at 1005, where a datapoint is received at a receipt time and has a raw time. As describedabove, the data point may be received at an intake system of a dataquantizer system, for example. The raw time may be assigned by theorigin data source and may be associated with a generation time for thedata point or a transmission time for the data point, for example.

At block 1010, a roll-up window for the data point may be identified,such as based on the raw time. The roll-up window may correspond to anassigned roll-up window for the data point, which may be associated withone include one or more other data points. In some cases, start timesand end times for the roll-up window may be determined. In some cases, aclose time for the roll-up window may be determined.

At 1015, the roll-up window may be evaluated to determine if the roll-upwindow is open for receiving new data points. This may be performed bycomparing the instant time with the roll-up close time, for example.This may be performed by comparing the receipt time for the data pointwith the roll-up close time. If the roll-up window is open for receivingnew data points (e.g., if the receipt time is before the close time),then the process may branch to block 1020, where the data point can beadded to the roll-up windows identified at block 1005. If the roll-upwindow is not open for receiving new data points (e.g., if the receipttime is after the close time), then the process may branch to 1025.

At 1025, the data point can be evaluated to determine if the data pointis received in-order (e.g., if the data point is in an in-ordercondition or an out-of-order condition). Whether the data point isreceived in-order can be performed by comparing the receipt time of thedata point with the receipt time of earlier received data points anddetermining if the receipt time of the data point is later than thereceipt time of all the other earlier received data points. If the datapoint is received in-order, method 1000 can branch again to block 1020,where the data point can be added to the roll-up windows. If the datapoint is received out-of-order, the process can branch to block 1030,where the data point can be dropped (e.g., the data point can beexplicitly not used for the roll-up windows). At block 1035, the roll-updata point can be published after the roll-up window closes.

Although method 1000 provides an overview of handling of data points fora single roll-up window, it will be appreciated that method 1000 may beapplicable to multiple roll-up windows simultaneously, such that one ormore or all aspects of method 1000 may be performed a plurality of timesfor a plurality of different roll-up windows. For example, if the datapoint corresponds to data generated once per second, the roll-up windowsthat may be identified at block 1010 may include a 5 second window, a 15second window, a 1 minute window, and a 5 minute window. Each of thesewindows will be evaluated for determination if the rollup window is openfor receiving new data points at block 1015. Where windows are open, theprocess may proceed to block 1020, as described above. For any windowsthat are closed, the process may proceed to block 1025, where thein-order state can be evaluated at block 1025 to determine whether tokeep a late data point, as in block 1020 for in-order data points, or todrop the data point, as in block 1030 for out-of-order data points.Because the different roll-up windows may each have different closetimes, this may mean that a data point may be dropped for one roll-upwindow but added to another roll-up window.

4.0. Terminology

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor, will cause a computingdevice to execute functions involving the disclosed techniques. In someembodiments, a carrier containing the aforementioned computer programproduct is provided. The carrier is one of an electronic signal, anoptical signal, a radio signal, or a non-transitory computer-readablestorage medium.

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.Furthermore, use of “e.g.,” is to be interpreted as providing anon-limiting example and does not imply that two things are identical ornecessarily equate to each other.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is understood with the context asused in general to convey that an item, term, etc. may be either X, Y orZ, or any combination thereof. Thus, such conjunctive language is notgenerally intended to imply that certain embodiments require at leastone of X, at least one of Y and at least one of Z to each be present.Further, use of the phrases “at least one of X, Y or Z” or “X, Y, and/orZ” as used in general is to convey that an item, term, etc. may beinclude X, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines or an isolated executionenvironment, rather than in dedicated computer hardware systems and/orcomputing devices. Likewise, the data repositories shown can representphysical and/or logical data storage, including, e.g., storage areanetworks or other distributed storage systems. Moreover, in someembodiments the connections between the components shown representpossible paths of data flow, rather than actual connections betweenhardware. While some examples of possible connections are shown, any ofthe subset of the components shown can communicate with any other subsetof components in various implementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

What is claimed is:
 1. A method performed by a data quantizer system fordynamically adjusting a maximum delay in publishing data points of atime series of data points, the method comprising: receiving a firstdata point of a time series of data points, the first data pointcomprising a first data point raw time indicating a time of creation ofthe first data point and a first receipt time indicating a time forwhich the first data point is obtained at the data quantizer system;deriving a first receipt delay time for the first data point, the firstreceipt delay time comprising a delay between the first receipt time anda first time associated with a first applicable window; responsive todetermining that the first receipt delay time is greater than any of aset of maximum delay values for the time series of data points: derivinga first time to live value specifying a time for the first receipt delaytime to be included in the set of maximum delay values, where a greaterdeviance between the first receipt delay time and a weighted movingaverage delay for the time series of data points reduces the first timeto live value; and updating the set of maximum delay values to includethe first receipt delay time and the first time to live value;publishing, at a time after an end of the first applicable window, thefirst data point to a streaming analytics engine; receiving a seconddata point, the second data point comprising a second data point rawtime indicating a time of creation of the second data point and a secondreceipt time indicating a time for which the second data point isobtained at the data quantizer system; deriving a second receipt delaytime for the second data point, the second receipt delay time comprisinga delay between the second receipt time and a second time associatedwith a second applicable window; responsive to determining that thefirst time to live value associated with the first receipt delay timehas expired: deriving a second time to live value based on the secondreceipt delay time; and updating the set of maximum delay values to addthe second receipt delay time and the second time to live value to theset of maximum delay values and remove the first receipt delay timeincluded in the set of maximum delay values; and publishing, at a timeafter an end of the second applicable window, the second data point tothe streaming analytics engine.
 2. The method of claim 1, furthercomprising: updating the weighted moving average delay and a delayvariance using data relating to the second data point.
 3. The method ofclaim 2, wherein updating the weighted moving average delay and thedelay variance further comprises: deriving a delta value as the secondreceipt delay time less an initial instance of the weighted movingaverage delay; deriving an updated weighted moving average delay bysummating the initial instance of the weighted moving average delay witha product of the delta value and an alpha value; and deriving the delayvariance as a product of the alpha value and an initial instance of thedelay variance, the alpha value, and the delta value.
 4. The method ofclaim 1, wherein deriving the first time to live value specifying a timefor the first receipt delay time to be included in the set of maximumdelay values further comprises: deriving a z-score as a quotient of thefirst receipt delay time less than the weighted moving average delay anda square root of a delay variance, wherein the z-score is used inderiving the first time to live value.
 5. The method of claim 1, whereinpublishing the first data point to the streaming analytics enginemodifies a graphical interface providing a graphical representation ofdata points with receipt values within the first applicable window. 6.The method of claim 1, wherein the first data point raw time is assignedto the first data point by a client device.
 7. A data quantizer systemfor dynamically adjusting a maximum delay in publishing data points of atime series of data points, the data quantizer system comprising: aprocessor; and a computer readable non-transitory storage medium storinginstructions that, when executed by the processor, cause the processorto: receive a first data point of a time series of data points, thefirst data point comprising a first data point raw time indicating atime of creation of the first data point and a first receipt timeindicating a time for which the first data point is obtained at the dataquantizer system; derive a first receipt delay time for the first datapoint, the first receipt delay time comprising a delay between the firstreceipt time and a first time associated with a first applicable window;responsive to determining that the first receipt delay time is greaterthan any of a set of maximum delay values for the time series of datapoints: derive a first time to live value specifying a time for thefirst receipt delay time to be included in the set of maximum delayvalues, where a greater deviance between the first receipt delay timeand a weighted moving average delay for the time series of data pointsreduces the first time to live value, and where the first receipt delaytime is removed from the set of maximum delay values after expiry of thetime to live value; and update the set of maximum delay values toinclude the first receipt delay time and the first time to live value;and publish, at a time after an end of the first applicable window, thefirst data point to a streaming analytics engine.
 8. The data quantizersystem of claim 7, wherein the instructions further cause the processorto: receive a second data point, the second data point comprising asecond data point raw time indicating a time of creation of the seconddata point and a second receipt time indicating a time for which thesecond data point is obtained at the data quantizer system; derive asecond receipt delay time for the second data point, the second receiptdelay time including a delay between the second receipt time and asecond time associated with a second applicable window; responsive todetermining that the first time to live value associated with the firstreceipt delay time has expired: derive a second time to live value basedon the second receipt delay time; and update the set of maximum delayvalues to add the second receipt delay time and the second time to livevalue to the set of maximum delay values and remove the first receiptdelay time included in the set of maximum delay values; and publish, ata time after an end of the second applicable window, the second datapoint to the streaming analytics engine.
 9. The data quantizer system ofclaim 8, wherein the instructions further cause the processor to: derivean initial instance of the weighted moving average delay and an initialinstance of a delay variance using data relating to the first datapoint.
 10. The data quantizer system of claim 9, wherein theinstructions further cause the processor to: update the weighted movingaverage delay and the delay variance, wherein the updating includes:derive a delta value as the second receipt delay time less an initialinstance of the weighted moving average delay; derive an updatedweighted moving average delay by summating the initial instance of theweighted moving average delay with a product of the delta value and analpha value; and derive the delay variance as a product of the alphavalue and an initial instance of the delay variance, the alpha value,and the delta value.
 11. The data quantizer system of claim 7, whereinderiving the first time to live value further comprises: derive az-score as a quotient of the first receipt delay time less than theweighted moving average delay and a square root of a delay variance,wherein the z-score is used in deriving the first time to live value.12. The data quantizer system of claim 11, wherein deriving the firsttime to live value comprising deriving a quotient of a maximumexpiration time and a maximum of a value of one and the z-score.
 13. Thedata quantizer system of claim 7, wherein deriving the first time tolive value further comprises determining whether the first time to livevalue is less than a minimum expiration time, wherein the time to livecomprises the minimum expiration time when the first time to live valueis less than the minimum expiration time.
 14. The data quantizer systemof claim 7, wherein publishing the first data point to the streaminganalytics engine modifies a graphical interface providing a graphicalrepresentation of data points with receipt values within the firstapplicable window.
 15. The data quantizer system of claim 7, wherein thefirst data point raw time is assigned to the first data point by aclient device.
 16. A computer readable non-transitory storage mediumstoring instructions for processing data generated by instrumentedsoftware, the instructions when executed by a processor cause theprocessor to perform a process comprising: receiving a first data pointof a time series of data points, the first data point comprising a firstdata point raw time indicating a time of creation of the first datapoint and a first receipt time indicating a time for which the firstdata point is obtained; deriving a first receipt delay time for thefirst data point, the first receipt delay time comprising a delaybetween the first receipt time and a first time associated with a firstapplicable window; responsive to determining that the first receiptdelay time is greater than any of a set of maximum delay values for thetime series of data points: deriving a first time to live valuespecifying a time for the first receipt delay time to be included in theset of maximum delay values; and updating the set of maximum delayvalues to include the first receipt delay time and the first time tolive value; publishing, at a time after an end of the first applicablewindow, the first data point to a streaming analytics engine; receivinga second data point, the second data point comprising a second datapoint raw time indicating a time of creation of the second data pointand a second receipt time indicating a time for which the second datapoint is obtained; deriving a second receipt delay time for the seconddata point, the second receipt delay time comprising a delay between thesecond receipt time and a second time associated with a secondapplicable window; responsive to determining that the first time to livevalue associated with the first receipt delay time has expired: derivinga second time to live value based on the second receipt delay time; andupdating the set of maximum delay values to add the second receipt delaytime and the second time to live value to the set of maximum delayvalues and remove the first receipt delay time included in the set ofmaximum delay values; and publishing, at a time after an end of thesecond applicable window, the second data point to the streaminganalytics engine.
 17. The computer readable non-transitory storagemedium of claim 16, wherein the process further comprises: updating aweighted moving average delay and a delay variance using data relatingto the second data point.
 18. The computer readable non-transitorystorage medium of claim 17, wherein updating the weighted moving averagedelay and the delay variance further comprises: deriving a delta valueas the second receipt delay time less an initial instance of theweighted moving average delay; deriving an updated weighted movingaverage delay by summating the initial instance of the weighted movingaverage delay with a product of the delta value and an alpha value; andderiving the delay variance as a product of the alpha value and aninitial instance of the delay variance, the alpha value, and the deltavalue.
 19. The computer readable non-transitory storage medium of claim17, wherein deriving the first time to live value specifying a time forthe first receipt delay time to be included in the set of maximum delayvalues further comprises: deriving a z-score as a quotient of the firstreceipt delay time less than the weighted moving average delay and asquare root of a delay variance, wherein the z-score is used in derivingthe first time to live value.
 20. The computer readable non-transitorystorage medium of claim 19, wherein deriving the first time to livevalue comprising deriving a quotient of a maximum expiration time and amaximum of a value of one and the z-score.